Open AccessRobust distributed model predictive control for uncertain networked control systems
Author(s) -
Zhang Langwen,
Wang Jingcheng,
Ge Yang,
Wang Bohui
Publication year - 2014
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2014.0311
Subject(s) - control theory (sociology) , model predictive control , flexibility (engineering) , robust control , convergence (economics) , computer science , stability (learning theory) , linear matrix inequality , robustness (evolution) , upper and lower bounds , control (management) , mathematical optimization , control system , mathematics , engineering , artificial intelligence , mathematical analysis , biochemistry , statistics , chemistry , machine learning , economic growth , electrical engineering , economics , gene
In this study, an approach to design robust distributed model predictive control (MPC) is proposed for polytopic uncertain networked control systems with time delays. To reduce the computational complexity and improve the flexibility, the entire system is decomposed into multiple smaller dimensional subsystems. For each subsystem, the proposed robust distributed MPC algorithm requires solving multiple linear matrix inequality optimisation problems to minimise an upper bound on a robust performance objective. An augmented polytopic uncertainty description is invoked to handle the input delays. The conservativeness of distributed MPC algorithm is reduced by utilising a sequence of feedback control laws. An iterative on‐line algorithm for robust distributed MPC is developed to coordinate the distributed MPC controllers. Convergence and robust stability of the proposed distributed MPC are investigated. A numerical example is carried out to demonstrate the effectiveness of the proposed algorithm.